Limnologists

Life, Physical & Social Sciences

AI exposure

  • Data source: BLSPublished: 2026-08

    High· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.061

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.40

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 39% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Exposed tasks only

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Disseminate information by writing reports and scientific papers or journal articles, and by making presentations and giving talks for schools, clubs, interest groups and park interpretive programs.

O*NET Task ID 1496

1.0
Study animals in their natural habitats, assessing effects of environment and industry on animals, interpreting findings and recommending alternative operating conditions for industry.

O*NET Task ID 1492

0.5
Inventory or estimate plant and wildlife populations.

O*NET Task ID 1493

0.5
Analyze characteristics of animals to identify and classify them.

O*NET Task ID 1494

0.5
Make recommendations on management systems and planning for wildlife populations and habitat, consulting with stakeholders and the public at large to explore options.

O*NET Task ID 1495

0.5
Study characteristics of animals, such as origin, interrelationships, classification, life histories, diseases, development, genetics, and distribution.

O*NET Task ID 1497

0.5
Perform administrative duties, such as fundraising, public relations, budgeting, and supervision of zoo staff.

O*NET Task ID 1498

0.5
Coordinate preventive programs to control the outbreak of wildlife diseases.

O*NET Task ID 1501

0.5
Inform and respond to public regarding wildlife and conservation issues, such as plant identification, hunting ordinances, and nuisance wildlife.

O*NET Task ID 18615

0.5
Check for, and ensure compliance with, environmental laws, and notify law enforcement when violations are identified.

O*NET Task ID 18616

0.5
Organize and conduct experimental studies with live animals in controlled or natural surroundings.

O*NET Task ID 1499

0.0
Prepare collections of preserved specimens or microscopic slides for species identification and study of development or disease.

O*NET Task ID 1502

0.0
Raise specimens for study and observation or for use in experiments.

O*NET Task ID 1503

0.0
Collect and dissect animal specimens and examine specimens under microscope.

O*NET Task ID 1504

0.0

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information